Richly classified and structured content enables many useful applications, including faceted browsing and filtered search, dynamically personalized web pages or books. This type of content may also be referred to as smart content (Gilbane in a smart content study (co-sponsored by IBM®)).
Common problems arising when using the content include having the content classified correctly, understanding relationships between classification values and enabling the values to be easy for users to select or navigate.
The Darwin Information Typing Architecture (DITA) 1.2 standard (OASIS DITA 1.2 specification, section on classification) provides a standardized solution addressing the problem of classifying and the problem of relating values with an extensible markup language (XML) format for classification schemes. DITA therefore enables XML data for authoring and publishing activities. The format provides control over values available to classifiers (such as original authors and publisher) and also provides a hierarchy for the values, which establishes relationships between the values. The hierarchy can also be used to expose values for user selection.
However, in instances in which classification schemes contain many facets and complex interdependencies, a typical user cannot be expected to select appropriate values to address a specific problem or a filtering scenario without a degree of guidance beyond that which is provided in a simple hierarchical browse.
Typically, in the case of complex schemes, the user is guided through a series of choices using a wizard or other form of coaching interface. This type of guidance typically solves the user problem but also creates maintenance and translation issues for the original authors and publishers. For example, maintenance issues occur when the classification scheme changes, because the guidance must be changed in the classification scheme, and also in the wizard for navigating or selecting from the scheme.
In another example, exploring websites through contextual facets uses dynamic generation of facets, which may be browsed, from discovered metadata in pages. The example however does not enable use of pre-existing taxonomies, or generation of wizards for complex facet selection. A further example deals with automatic personalization of content by dynamic selection of facets, with minimal interaction from a user. The further example does not address management of taxonomies or the design of guided selection experiences.
Another solution example provides a capability to navigate any given hierarchically organized value set, but does not provide a method for generating a guided selection experience (for example, a wizard) to handle complex facet selection scenarios, including inter-related facets. In a similar example, a capability is provided to navigate and select from a combination of facets and tags; however guided selection or single sourcing between a wizard and a taxonomy source is not provided. A further mechanism provides a capability of merging of multiple facets for consolidated search across domains using a mix of different facets. However guided selection or single sourcing between a wizard and a taxonomy source is not provided by the mechanism.
Another mechanism purported to resolve the problems provides a capability for deploying an enterprise-wide taxonomy, with methods for identifying relationships between nodes in the taxonomy that could be used to affect interface display. However the mechanism does not provide support for single sourcing of a guided selection interface and a taxonomy. A similar mechanism provides a capability for guided navigation of search results using taxonomies, potentially derived from a variety of sources including dynamic analysis, and ontologies. The described mechanism does not include guided navigation for the initial selection of a set of related terms, using the taxonomy document itself as a single source for both an allowed value list used by a search engine and a wizard interface used to guide the user through the selection process.